Today, a black hole observed … tomorrow, a pothole avoided?
On Wednesday, researchers with the event, Horizon Telescope project released the first images ever taken of a black hole – a gravitational sinkhole in space that is powerful enough to suck in even light itself. Capturing these images was an amazing feat. But the technologies developed to produce images of supermassive void 55 million light years away could end up having far-reaching impacts back on Earth.
First theorized by Albert Einstein, black holes have previously been recorded only by the gap they left in our data. In 2001, for example, scientists announced that the Hubble Space Telescope observed ultraviolet light becoming dimmer and eventually disappeared altogether as it fell into the black hole Cygnus XR-1
To get a picture of the black hole itself, The EHT project used a network of 10 Earthbound radio telescopes, linked together to function as a single system.
While the task of coordinating telescopes and collecting radio signals was complex and impressive, it's the development of algorithms. That is likely to have long-term impacts on technology, said Jonathan Weintroub, an electrical engineer who developed the physical instrumentation for the EHT project. His team used off-the-shelf products and existing telescopes to essentially build a globe-spanning telescope like a kid could build a Lego model. That's no small feat. The final system was able to collect and store 5 petabytes of data. If 1 byte were a 2-foot-by-2-foot tile, then 1 petabyte would cover the whole Earth.
The problem: That global megatelescope (while obviously awesome) is still producing data as holey as a slice of Swiss cheese. The telescopes are collecting photons – packets of light – that fall from space like the proverbial pennies from heaven. But even working together, they can only catch a small sampling of those photons. Reconstructing an image from that sparse data set represents a challenge as massive as the black hole itself, Weintroub told me. The algorithms of EHT researchers were critical to solving that challenge, and their solution could have wide-ranging implications.
Imagine trying to put together a puzzle with 90 percent of the missing pieces. Not only is it hard to assemble that image correctly, it ends up being hard to even know what image you're trying to make. "Since we have such sparse measurements, there tends to be an infinite number of images that can match data," said Lindy Blackburn, an MIT graduate student who works as a data scientist on the EHT project.
The algorithms the EHT scientists built help to restrain that infinite number of possible images by sorting out which results were physically plausible and which were wildly unlikely. For example, Blackburn told me that the algorithms all tended to favor the images that could explain the measurements taken by the telescopes in the simplest possible way, weeding out the images with lots of fine details or complex features. When they applied a list of limitations like that, putting the puzzle together correctly (or, at least, realistically) became a little less hard. It's not perfect – the image of the black hole is blurry, Blackburn told me, partly because each of the four teams produced a slightly different image and the researchers were slightly conservative in choosing which details would make it the final, representative image. But it was enough to turn the radio wave data into a picture.
And that matters, Blackburn told me, because astronomy is not the only field facing the problem of converting sparse data into images. It comes in medical imaging, for example, when doctors use MRIs to convert radio waves into pictures of your body. It's also a key part of self-driving cars, which rely on computer visualization to "see" everything from potholes to people. The types of algorithms have been developed to photograph a black hole built on that research from other fields and in turn could help improve the way the computers live on Earth. This blurry image of a dark whirlpool in space could end up as a chapter in the story of how technological developments allowed humans to safely ride in the 2-ton hunks of metal and plastic propelled by computers alone.
If that happens, Weintraub said , it will not just be the technology that changes the future, it will also be the people who made it. Many of the people working on imaging technology for the EHT project, like Blackburn, are graduate students. Taking a picture of a black hole did not just mean developing some cool tech – it meant empowering a bunch of early-career scientists to come up with different ideas and get really good at creating new tools right before they dispersed across academia and industry. . Blackburn's colleague, Katie Bouman, for example, worked in MIT's computer vision lab as part of a postdoctoral fellowship, collaborating on algorithm improvement across many different fields and computer vision applications. Part of the team that developed the EHT's "eyes", she's set to start her first professorship at Caltech this fall. The project's role as an incubator of scientific talent could end up being its largest contribution.